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Title: Online feature selection for model-based reinforcement learning
Authors: Nguyen, T.T.
Li, Z.
Silander, T.
Leong, T.-Y. 
Issue Date: 2013
Citation: Nguyen, T.T.,Li, Z.,Silander, T.,Leong, T.-Y. (2013). Online feature selection for model-based reinforcement learning. 30th International Conference on Machine Learning, ICML 2013 (PART 1) : 498-506. ScholarBank@NUS Repository.
Abstract: We propose a new framework for learning the world dynamics of feature-rich environments in model-based reinforcement learning. The main idea is formalized as a new, factored state-transition representation that supports efficient online-learning of the relevant features. We construct the transition models through predicting how the actions change the world. We introduce an online sparse coding learning technique for feature selection in high-dimensional spaces. We derive theoretical guarantees for our framework and empirically demonstrate its practicality in both simulated and real robotics domains. Copyright 2013 by the author(s).
Source Title: 30th International Conference on Machine Learning, ICML 2013
Appears in Collections:Staff Publications

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